Context. The problem of estimating the software size in the early stage of a software project is important, since the information<br />obtained from estimating the software size is used for predicting the software development effort, including open-source Java-based<br />information systems. The object of the study is the process of estimating the software size of open-source Java-based information<br />systems. The subject of the study is the regression models for estimating the software size of open-source Java-based information<br />systems.<br />Objective. The goal of the work is the creation of the non-linear regression model for estimating the software size of open-source<br />Java-based information systems on the basis of the Johnson multivariate normalizing transformation.<br />Method. The model, confidence and prediction intervals of multiply non-linear regression for estimating the software size of<br />open-source Java-based information systems are constructed on the basis of the Johnson multivariate normalizing transformation for<br />non-Gaussian data with the help of appropriate techniques. The techniques to build the models, equations, confidence and prediction<br />intervals of non-linear regressions are based on the multiple non-linear regression analysis using the multivariate normalizing<br />transformations. The appropriate techniques are considered. The techniques allow to take into account the correlation between<br />random variables in the case of normalization of multivariate non-Gaussian data. In general, this leads to a reduction of the mean<br />magnitude of relative error, the widths of the confidence and prediction intervals in comparison with the linear models or nonlinear<br />models constructed using univariate normalizing transformations.<br />Results. Comparison of the constructed model with the linear model and non-linear regression models based on the decimal<br />logarithm and the Johnson univariate transformation has been performed.<br />Conclusions. The non-linear regression model to estimate the software size of open-source Java-based information systems is<br />constructed on the basis of the Johnson multivariate transformation for SB family. This model, in comparison with other regression<br />models (both linear and non-linear), has a larger multiple coefficient of determination, a larger value of percentage of prediction and<br />a smaller value of the mean magnitude of relative error. The prospects for further research may include the application of other<br />multivariate normalizing transformations and data sets to construct the non-linear regression model for estimating the software size<br />of open-source Java-based information systems.
Науковий журнал «Радіоелектроніка, інформатика, управління»
Переглянути архів Інформація| Поле | Співвідношення | |
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dc |
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Prykhodko, N. V.; Admiral Makarov National University of Shipbuilding, Mykolaiv, Ukraine. Prykhodko, S. B.; Admiral Makarov National University of Shipbuilding, Mykolaiv, Ukraine. |
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Context. The problem of estimating the software size in the early stage of a software project is important, since the information<br />obtained from estimating the software size is used for predicting the software development effort, including open-source Java-based<br />information systems. The object of the study is the process of estimating the software size of open-source Java-based information<br />systems. The subject of the study is the regression models for estimating the software size of open-source Java-based information<br />systems.<br />Objective. The goal of the work is the creation of the non-linear regression model for estimating the software size of open-source<br />Java-based information systems on the basis of the Johnson multivariate normalizing transformation.<br />Method. The model, confidence and prediction intervals of multiply non-linear regression for estimating the software size of<br />open-source Java-based information systems are constructed on the basis of the Johnson multivariate normalizing transformation for<br />non-Gaussian data with the help of appropriate techniques. The techniques to build the models, equations, confidence and prediction<br />intervals of non-linear regressions are based on the multiple non-linear regression analysis using the multivariate normalizing<br />transformations. The appropriate techniques are considered. The techniques allow to take into account the correlation between<br />random variables in the case of normalization of multivariate non-Gaussian data. In general, this leads to a reduction of the mean<br />magnitude of relative error, the widths of the confidence and prediction intervals in comparison with the linear models or nonlinear<br />models constructed using univariate normalizing transformations.<br />Results. Comparison of the constructed model with the linear model and non-linear regression models based on the decimal<br />logarithm and the Johnson univariate transformation has been performed.<br />Conclusions. The non-linear regression model to estimate the software size of open-source Java-based information systems is<br />constructed on the basis of the Johnson multivariate transformation for SB family. This model, in comparison with other regression<br />models (both linear and non-linear), has a larger multiple coefficient of determination, a larger value of percentage of prediction and<br />a smaller value of the mean magnitude of relative error. The prospects for further research may include the application of other<br />multivariate normalizing transformations and data sets to construct the non-linear regression model for estimating the software size<br />of open-source Java-based information systems. |
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Zaporizhzhya National Technical University 2018-12-07 16:07:43 |
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application/pdf http://ric.zntu.edu.ua/article/view/150003 |
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Radio Electronics, Computer Science, Control; No 3 (2018): Radio Electronics, Computer Science, Control |
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en |
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Copyright (c) 2018 N. V. Prykhodko, S. B. Prykhodko |
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